## Our World In Data Vaccine Plots
rm(list=ls())
source("../DATA/movavg.R")
db <- db <- dbConnect(RSQLite::SQLite(),dbname= "../COVID-19-DB/OURWORLD.sqlite3")
SWE <- dbGetQuery(db,"select * from OWID")
SWE$date <- as.Date(SWE$date)
SWE <- SWE[order(SWE$date),]
df <- SWE %>%select(date,location,iso_code,new_vaccinations,
new_vaccinations_smoothed,total_vaccinations,population)
dbDisconnect(db)
US Vaccnations By Date
US <- df %>% filter(location =="United States") %>% na.omit()
summary(US$new_vaccinations)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 57909 651669 878059 886713 1125552 1561585
p5 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="US New Vaccinations By Date") +
geom_smooth(aes(x=date,y=new_vaccinations))
p6 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="US Total Vaccinations By Date")
US$Rate <- US$total_vaccinations/US$population
p7 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
scale_y_continuous(labels=percent) +
labs(title="US vaccination Rate By Day")
ggplotly(p5)
ggplotly(p6)
ggplotly(p7)